Search for: All records

Attracting students to computing is crucial for advancing the development of new skills and fostering positive attitudes toward the field, especially among females and minoritized populations. One promising approach involves integrating computing with artistic activities, such as music. This study examines how learner’s prior experiences influence their participation in a virtual summer camp on coding with music. The study also examines how participation in the camp influences participants' attitudes about computing, with an eye toward gender differences. Data were collected through participant surveys (N=73) and focus groups (N=48). Findings suggest that parents’ and guardians' involvement is crucial for participation and integrating coding with artistic work holds promise for attracting students to the field. Findings can inform possible paths to engaging students in computing. 

Attracting students to computing is crucial for advancing the development of new skills and fostering positive attitudes toward the field, especially among females and minoritized populations. One promising approach involves integrating computing with artistic activities, such as music. This study examines how learner’s prior experiences influence their participation in a virtual summer camp on coding with music. The study also examines how participation in the camp influences participants' attitudes about computing, with an eye toward gender differences. Data were collected through participant surveys (N=73) and focus groups (N=48). Findings suggest that parents’ and guardians' involvement is crucial for participation and integrating coding with artistic work holds promise for attracting students to the field. Findings can inform possible paths to engaging students in computing. 

Attracting students to computing is crucial for advancing the development of new skills and fostering positive attitudes toward the field, especially among females and minoritized populations. One promising approach involves integrating computing with artistic activities, such as music. This study examines how learner’s prior experiences influence their participation in a virtual summer camp on coding with music. The study also examines how participation in the camp influences participants' attitudes about computing, with an eye toward gender differences. Data were collected through participant surveys (N=73) and focus groups (N=48). Findings suggest that parents’ and guardians' involvement is crucial for participation and integrating coding with artistic work holds promise for attracting students to the field. Findings can inform possible paths to engaging students in computing. 

Broadening participation in computer science has been widely stud- ied, creating many diferent techniques to attract, motivate, and engage students. A common meta-strategy is to use an outside do- main as a hook, using the concepts in that domain to teach computer science. These domains are selected to interest the student, but stu- dents often lack a strong background in these domains. Therefore, a strategy designed to increase students’ interest, motivation, and engagement could actually create more barriers for students, who now are faced with learning two new topics. To reduce this poten- tial barrier in the domain of music, this paper presents the use of automated, immediate feedback during programming activities at a summer camp that uses music to teach foundational programming concepts. The feedback guides students musically, correcting notes that are out-of-key or rhythmic phrases that are too long or short, allowing students to focus their learning on the computer science concepts. This paper compares the correctness of students that re- ceived automated feedback with students that did not, which shows the efectiveness of the feedback. Follow up focus groups with stu- dents confrmed this quantitative data, with students claiming that the feedback was not only useful but that the activities would be much more challenging without the feedback. 

Researchers and practitioners have demonstrated various benefits of introducing computational thinking (CT) through music com- position coding. While researchers have studied the impacts on participant attitudes towards CT and their learning of CT concepts, more case studies are needed on both learning CT concepts as well as CT practices, i.e., the processes of constructing music coding projects. This paper presents a case study of middle schoolers in an informal learning environment focused on integrating music composition with coding in TunePad. Specifically, we collected and analyzed logs of coding events, final code products, and surveys to explore both CT concept use and CT practices exhibited by the par- ticipants as they completed open-ended music coding activities to create their own melodies with specific music and CT requirements and recommendations 

Software engineers are crowdsourcing answers to their everyday challenges on Q&A forums (e.g., Stack Overflow) and more recently in public chat communities such as Slack, IRC, and Gitter. Many software-related chat conversations contain valuable expert knowledge that is useful for both mining to improve programming support tools and for readers who did not participate in the original chat conversations. However, most chat platforms and communities do not contain built-in quality indicators (e.g., accepted answers, vote counts). Therefore, it is difficult to identify conversations that contain useful information for mining or reading, i.e., conversations of post hoc quality. In this article, we investigate automatically detecting developer conversations of post hoc quality from public chat channels. We first describe an analysis of 400 developer conversations that indicate potential characteristics of post hoc quality, followed by a machine learning-based approach for automatically identifying conversations of post hoc quality. Our evaluation of 2,000 annotated Slack conversations in four programming communities (python, clojure, elm, and racket) indicates that our approach can achieve precision of 0.82, recall of 0.90, F-measure of 0.86, and MCC of 0.57. To our knowledge, this is the first automated technique for detecting developer conversations of post hoc quality.